In this project we propose to develop predictive "regulation signatures" which should overcome shortcomings of existing methods of molecular diagnostics. As a proof of concept, in Phase I we will develop regulation signatures for the sensitivity of cancer cell lines to the inhibitors of EGF receptor and will test their accuracy using publicly available datasets. This goal will be accomplished in two stages. First, we will build and optimize focused network model for global Erb family signaling using well-defined training data and knowledge content. In the second step we will test performance of the developed Erb signaling network model on publicly available sets of gene expression profiles from the variety of non-small cell lung carcinoma cell lines with variable drug sensitivity. Analysis will be performed to identify sets of key signaling proteins associated with drug resistance. Using these proteins predictive "regulation signatures" will be developed and their performance will be tested. If successful, the methodology could be replicated for developing predictive models for sensitivity to a broad range of targeted therapies, leading to a number of diagnostic applications such as specialized molecular tests, systems for formulating combination therapies and procedures for selecting patient cohorts for clinical trials. PUBLIC HEALTH RELEVANCE: In this project we will develop novel "regulation signatures" to predict sensitivity to the inhibitors of EGF receptor and identify mechanisms of drug resistance. Project will utilize public gene expression data in combination with knowledge base on protein interactions and our recently developed network analysis algorithm. If successful, the methodology could lead to a number of diagnostic applications.